Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording

Bueno-Barrachina, Jose-M.; Ye-Lin, Yiyao; Nieto-del-Amor, Félix; Fuster-Roig, Vicente

doi:10.1016/j.engappai.2022.105799

Cited by 11 publications

(3 citation statements)

References 47 publications

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“…Convolutional Neural Networks (CNNs) are a form of deep learning technique that has found widespread use in image and video analysis [35]. CNNs can identify complex patterns in the data that are not easily noticeable by a human operator [36,37] and are capable of managing vast amounts of data, making them suitable for industrial applications where massive amounts of sensor data are generated [38]. However, CNNs need labelled data and struggle to effectively handle complex datasets when the data are homogeneous and multi-channel [39,40].…”

Section: Literature Reviewmentioning

confidence: 99%

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

Bampoula,

Nikolakis,

Alexopoulos

2024

Sensors

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The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with artificial intelligence methods to create insight on the condition of production equipment, hence potentially increasing the sustainability of existing manufacturing and production systems, by optimizing resource utilization, waste, and production downtime. In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to enable the forecasting of asset failures through spatial and temporal time series. These neural networks are implemented into a software prototype. The dataset used for training and testing the models is derived from a metal processing industry case study. Ultimately, the goal is to train a remaining useful life (RUL) estimation model.

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Section: Literature Reviewmentioning

confidence: 99%

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

Bampoula,

Nikolakis,

Alexopoulos

2024

Sensors

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“…The classes can be divided into various levels, ranging from low and safe levels to high and severe levels, indicating the likelihood of failure. The authors of [124] introduced a novel algorithm based on one-dimensional CNN for predicting the leakage current of insulators using environmental data. The authors conducted an analysis using historical data and demonstrated that a set of 21 weather condition samples served as an adequate number of features for the regression task, resulting in accurate predictions.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids

Mirshekali,

Santos,

Shaker

2023

Energies

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The maintenance of electrical grids is crucial for improving their reliability, performance, and cost-effectiveness. It involves employing various strategies to ensure smooth operation and address potential issues. With the advancement of digital technologies, utilizing time-series prediction has emerged as a valuable approach to enhance maintenance practices in electrical systems. The utilization of various recorded data from electrical grid components plays a crucial role in digitally enabled maintenance. However, the comprehensive exploration of time-series data prediction for maintenance is still lacking. This review paper extensively explores different time series that can be utilized to support maintenance efforts in electrical grids with regard to different maintenance strategies and grid components. The digitization of the electrical grids has enabled the collection of diverse time-series data from various network components. In this context, the paper provides an overview of how these time-series and historical-fault data can be utilized for maintenance purposes in electrical grids. Various maintenance levels and time series used for maintenance purposes in different components of the electrical grid are presented.

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“…It is well-known the accumulation of contamination and moisture on the surface of insulators is the main factor leading to surface flashover. − Conventional antipollution flashover measures, such as manual cleaning, adjusting creepage distances, and using antifouling and semiconductor glazed insulators, have proven unsatisfactory due to their high costs, technical limitations, and lack of efficiency. , One important measure employed in modern power grids involves the application of antipollution flashover composite coatings to insulator surfaces. This includes the use of room temperature vulcanizing silicone rubber (RTV) or permanent room temperature vulcanized silicone rubber (PRTV) to achieve the desired effect .…”

Section: Introductionmentioning

confidence: 99%

Robust and Durable Superhydrophobic Coatings with Antipollution Flashover Performance via Silane-Modified Polyurea

Li,

Tan,

Chen

et al. 2024

Langmuir

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The inadequate hydrophobicity and the degradation in usage seriously hampered the applications of the existing antipollution flashover coatings. In this paper, a superhydrophobic polyurea coating with antipollution flashover ability was fabricated through chemically grafting the silica onto the chains of polyurea by utilizing silane coupling agent and hydrophobic modification. It is demonstrated that the coating exhibits outstanding antipollution flashover performances. Noteworthy, the surface pollution flashover voltage has been increased by 33.8% compared with the room temperature vulcanizing silicone rubber (RTV silicone rubber). In addition, the volume resistivity is above 1.0 × 10 12 Ω•m, and the dielectric strength achieves to 28.85 kV/mm, which represents excellent insulating property. Furthermore, the superhydrophobic polyurea coating exhibits outstanding abrasion resistance, adhesion, acid−base resistance, and durability. As a result, it holds great promise for use in preventing pollution flashover in electrical insulators.

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Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording

Cited by 11 publications

References 47 publications

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids

Robust and Durable Superhydrophobic Coatings with Antipollution Flashover Performance via Silane-Modified Polyurea

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